论文标题
在分布式模型预测控制中，基于一致性约束的方法，用于耦合状态约束
A Consistency Constraint-Based Approach to Coupled State Constraints in Distributed Model Predictive Control
论文作者
论文摘要
在本文中，我们为动态解耦系统提供了一个分布式模型预测控制（DMPC）方案，该方案受状态约束，耦合状态约束和输入约束。在拟议的控制方案中，邻居到邻居的通信足够，所有子系统并行解决了其局部优化问题。该方法依赖于一致性约束，该约束定义了每个子系统参考轨迹周围的邻域，在该轨迹中保证了相应的子系统的状态。参考轨迹和一致性约束是相邻子系统的已知。与其他相关方法相反，参考轨迹迭代得以改进。此外，即使在存在非凸状状态约束的情况下，提出的方法也允许提出凸优化问题。通过模拟证明了算法的有效性。
In this paper, we present a distributed model predictive control (DMPC) scheme for dynamically decoupled systems which are subject to state constraints, coupling state constraints and input constraints. In the proposed control scheme, neighbor-to-neighbor communication suffices and all subsystems solve their local optimization problem in parallel. The approach relies on consistency constraints which define a neighborhood around each subsystem's reference trajectory where the state of the respective subsystem is guaranteed to stay in. Reference trajectories and consistency constraints are known to neighboring subsystems. Contrary to other relevant approaches, the reference trajectories are improved iteratively. Besides, the presented approach allows the formulation of convex optimization problems even in the presence of non-convex state constraints. The algorithm's effectiveness is demonstrated with a simulation.
